In this article we will see how we can get the center of mass of the image in mahotas. Center of mass” (for binary images) is a bit convoluted way of saying “mean value across each dimension”. In other words – take all x coordinates and average them – and you got x coordinate of your “center of mass”, the same for y.
In this tutorial we will use “lena” image, below is the command to load it.
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use mahotas.center_of_mass method
Syntax : mahotas.center_of_mass(img)
Argument : It takes image object as argument
Return : It returns center of mass co-ordinates
Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Below is the implementation
Python3
# importing required libraries# importing required librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as np # loading imageimg = mahotas.demos.load('lena')# grey imageg = img[:, :, 1]# multiplying grey image valuesg = g * 100 # filtering imageimg = img.max(2) # showing imageimshow(img)show()# getting center of masscenter = mahotas.center_of_mass(img) # printing center of mass co-ordinateprint("Center of Mass : " + str(center)) |
Output :
Center of Mass : [246.64854256 259.45157125]
Another example
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport os # loading imageimg = mahotas.imread('dog_image.png') # filtering imageimg = img[:, :, 0] # showing imageimshow(img)show()# getting center of masscenter = mahotas.center_of_mass(img) # printing center of mass co-ordinateprint("Center of Mass : " + str(center)) |
Output :
Center of Mass : [265.35619268 482.66701402]
